Determining question and answer alternatives

ABSTRACT

A computer-implemented method can include identifying one or more candidate topics from a query. The method can generate, for each candidate topic, a candidate topic-answer pair that includes both the candidate topic and an answer to the query for the candidate topic. The method can obtain search results based on the query, wherein one or more of the search results references an annotated resource. For each candidate topic-answer pair, the method can determine a score for the candidate topic-answer pair for use in determining a response to the query, based on (i) an occurrence of the candidate topic in the annotations of the resources referenced by one or more of the search results, and (ii) an occurrence of the answer in annotations of the resources referenced by the one or more search results, or in the resources referenced by the one or more search results.

TECHNICAL FIELD

This document generally relates to search engines.

BACKGROUND

Many people use the Internet to obtain information. For example, usersmay submit queries about a topic for which they want information. Insome situations, these queries can be submitted to a search system thatis configured to search a collection of documents to obtain informationpertaining to the queries. In some instances, the queries can be acollection of words that are submitted to the search system usingBoolean operators (e.g., OR and AND) or Natural Language formulations toperform the search. Some queries may indicate that the user is searchingfor a particular fact to answer a question reflected in the query.

SUMMARY

When a question-and-answer (Q&A) system receives a query, such as in thesearch context, the system must interpret the query, determine whetherto respond, and if so, select one or more answers with which to respond.Not all queries may be received in the form of a question, and somequeries might be vague or ambiguous. For example, a query that recites“Washington's age” is not provided in the form of a question, and thesubject matter of the query is vague. Washington could refer, forexample, to President George Washington, actor Denzel Washington, thestate of Washington, or Washington D.C. The Q&A system would then haveto determine whether the user who provided the query is likely to beinterested in receiving a factual answer relevant to the query, and ifso, what the query most likely refers to or is asking. The techniquesdescribed in this paper describe systems and methods for determiningwhether to respond to a query with one or more factual answers,including how to rank multiple candidate topics and answers in a waythat indicates the most likely interpretation(s) of a query.

In one implementation, a computer-implemented method can includeidentifying one or more candidate topics from a query. The method cangenerate, for each candidate topic, a candidate topic-answer pair thatincludes both the candidate topic and an answer to the query for thecandidate topic. The method can obtain search results based on thequery, wherein one or more of the search results references an annotatedresource, wherein an annotated resource is a resource that, based on anautomated evaluation of the content of the resource, is associated withan annotation that identifies one or more likely topics associated withthe resource. For each candidate topic-answer pair, the method candetermine a score for the candidate topic-answer pair based on (i) anoccurrence of the candidate topic in the annotations of the resourcesreferenced by one or more of the search results, and (ii) an occurrenceof the answer in annotations of the resources referenced by the one ormore search results, or in the resources referenced by the one or moresearch results. The method can also include determining whether torespond to the query with one or more answers from the candidatetopic-answer pairs, based on the scores.

These and other implementations described herein can optionally includeone or more of the following features. The method can determine whetherto respond to the query by comparing one or more the scores to apredetermined threshold. The method can further include selecting, basedon the comparison of the one or more scores to the predeterminedthreshold, one or more answers to respond to the query from among thecandidate topic-answer pairs. The method can also determine, based onthe comparison of the one or more scores to the predetermined threshold,to not respond to the query with an answer from among the candidatetopic-answer pairs. In some implementations, the method can select,based on the scores, one or more answers to respond to the query fromamong the candidate topic-answer pairs. The one or more selected answersmay include an answer from the candidate topic-answer pair that has ahighest score among the scores of each of the candidate topic-answerpairs. The selected one or more answers can be provided to a user at aclient computing device.

In some implementations, each candidate topic can be represented by anode in a graph of interconnected nodes that each represents a knowntopic. Generating, for each candidate topic, a candidate topic-answerpair that includes the candidate topic and an answer to the query forthe candidate topic can include identifying an attribute value from thenode that represents each candidate topic, respectively. The answer inone or more of the candidate topic-answer pairs can be represented by anode in the graph of interconnected nodes.

In some implementations, the score can be further based on a respectivequery relevance score of the search results that include annotations inwhich the candidate topic occurs. The method can also base the scoresfor one or more of the candidate topic-answer pairs on a confidencemeasure associated with each of one or more annotations in which thecandidate topic in a respective candidate topic-answer pair occurs, oreach of one or more annotations in which the answer in a respectivecandidate topic-answer pair occurs.

In another implementation, a computer system can include one or morecomputing devices and an interface at the one or more computing devicesthat is programmed to receive a query. The system can include aknowledge repository that is accessible to the one or more computingdevices and that includes a plurality of topics, each topic includingone or more attributes and associated values for the attributes. Amapping module that is installed on the one or more computing devicescan identify one or more candidate topics from the topics in theknowledge repository, wherein, the identified candidate topics aredetermined to relate to a possible subject of the query. An answergenerator that is installed on the one or more computing devices cangenerate, for each candidate topic, a candidate topic-answer pair thatincludes (i) the candidate topic, and (ii) an answer to the query forthe candidate topic, wherein the answer for each candidate topic isidentified from information in the knowledge repository. A search enginethat is installed on the one or more computing devices and can returnsearch results based on the query, wherein one or more of the searchresults references an annotated resource. An annotated resource is aresource that, based on an automated evaluation of the content of theresource, is associated with an annotation that identifies one or morelikely topics associated with the resource. A scoring module installedon the one or more computing devices can determine a score for eachcandidate topic-answer pair based on (i) an occurrence of the candidatetopic in the annotations of the resources referenced by one or more ofthe search results, and (ii) an occurrence of the answer in annotationsof the resources referenced by the one or more search results, or in theresources referenced by the one or more search results. A front-endsystem at the one or more computing devices can determine whether torespond to the query with one or more answers from the candidatetopic-answer pairs, based on the scores.

These and other implementations described herein can optionally includeone or more of the following features. The front end system candetermine whether to respond to the query based on a comparison of oneor more of the scores to a predetermined threshold. Each of theplurality of topics that is included in the knowledge repository can berepresented by a node in a graph of interconnected nodes. The one ormore returned search results from the search engine can be associatedwith a respective query relevance score and the score can be determinedby the scoring module for each candidate topic-answer pair based on thequery relevance scores of one or more of the search results thatreference an annotated resource in which the candidate topic occurs. Forone or more of the candidate topic-answer pairs, the score can befurther based on a confidence measure associated with each of one ormore annotations in which the candidate topic in a respective candidatetopic-answer pair occurs, or each of one or more annotations in whichthe answer in a respective candidate topic-answer pair occurs.

In one implementation, a tangible computer-readable storage device canhave instructions stored thereon that, when executed by one or morecomputer processors, cause the processors to perform operations. Theoperations can include identifying one or more candidate topics from aquery and generating for each candidate topic, a candidate topic-answerpair that includes the candidate topic and an answer to the query forthe candidate topic. The operations can further include obtaining searchresults based on the query, wherein one or more of the search resultsreferences an annotated resource, wherein an annotated resource is aresource that, based on an automated evaluation of the content of theresource, is associated with an annotation that identifies one or morelikely topics associated with the resource. For each candidatetopic-answer pair, the operations can determine a score for thecandidate topic-answer pair based on (i) an occurrence of the candidatetopic in the annotations of the resources referenced by one or more ofthe search results, and (ii) an occurrence of the answer in annotationsof the resources referenced by the one or more search results, or in theresources referenced by the one or more search results. The operationscan also include determining whether to respond to the query with one ormore answers from the candidate topic-answer pairs, based on the scores.

These and other implementations described herein can optionally includeone or more of the following features. Determining whether to respond tothe query can include comparing one or more of the scores to apredetermined threshold. The operations can include determining a queryrelevance score for one or more of the returned search results, whereinthe score for one or more of the candidate topic-answer pairs is furtherbased on the query relevance scores of one or more of the search resultsthat reference an annotated resource in which the candidate topicoccurs. For one or more of the candidate topic-answer pairs, the scorecan further be based on a confidence measure associated with (i) each ofone or more annotations in which the candidate topic in a respectivecandidate topic-answer pair occurs, or (ii) each of one or moreannotations in which the answer in a respective candidate topic-answerpair occurs.

Particular implementations of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. Candidate responses to the query can be scored sothat a Q&A system or method can determine whether to provide a responseto the query. If the query is not asking a question or none of thecandidate answers are sufficiently relevant to the query, then noresponse may be provided. Additionally, the techniques described hereincan interpret a vague or ambiguous query and provide a response that ismost likely to be relevant to what a user desired in submitting thequery.

The details of one or more implementations of the subject matterdescribed in this specification are set forth in the accompanyingdrawings and the description below. Other features, aspects, andadvantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a flow diagram of an example topic-answer selectionprocess.

FIG. 2 depicts an example system for determining one or moretopic-answer pairs for responding to a query.

FIG. 3 depicts an example method for determining whether and how torespond to a query with one or more topic-answer pairs.

FIG. 4 depicts an example search result page including a selectedtopic-answer pair provided to a user.

FIG. 5 depicts an example data graph of interconnected nodes thatrepresent topics in a knowledge base.

FIG. 6 is a block diagram of computing devices that may be used toimplement the systems and methods described in this document.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This document generally describes techniques for responding to factualquestions from a query. In some instances, when a user provides a queryat a search interface, it is most helpful to the user to responddirectly with one or more facts that answer a question determined to berelevant to the query. For example, in a query that asked, “How tall isMount Everest?”, responding directly with an answer (e.g., “MountEverest has an elevation of 29,029 feet”) may be more useful than a pageof search results that requires the user to follow the results to adocument referenced by the search results to find the answer therein. Aquestion-and-answer (Q&A) system can be used to respond to such queriesas described.

The flow diagram depicted in FIG. 1 shows an example method 100 that canreceive a query 112 and provide a response 124 that a Q&A systemdetermines to be relevant to the query 112. The method can beimplemented by any of a variety of computing devices/systems, such as aQ&A system 200, which is described further below for FIG. 2.

The method 100 can receive a query 112. The query 112 may be received inany of a variety of ways, include a range of content, and may bestructured in various forms. For instance, a user at a client computingdevice may have a specific question in mind and submit the query 112 toa search interface that submits queries to one or more systems such as aQ&A system 200 and/or a web search engine. In some implementations, thequery 112 may be provided as text, or may be transcribed to text basedon user voice input. In other implementations, the user need not be aperson or the query 112 need not be received from a user. For instance,a computer system may generate queries 112 to be provided in batch, andthe queries may include one or more words, phrases, clauses, orquestions. The example query 112 depicted in FIG. 1 asks the question,“How long is Harry Potter?” This query 112 is in the form of a question,which can indicate that the user who provided the query 112 is likelysearching for one or more facts to answer the question.

The query 112 can be vague or ambiguous. For example, the method of FIG.1 can identify “Harry Potter” as the subject of the query 112. But theterms “Harry Potter” are ambiguous, as they may refer to one or moreparticular topics such as any of the seven books in the Harry Potterfranchise, any of the film adaptations of the books, or a ride, themepark, audiobook, cartoon, etc. associated with the Harry Potterfranchise. The query 112 could also refer to the Harry Potter characteritself. Depending on which topic the user intended to refer to in thequery 112, a different answer can apply, or even different types ofanswers. Thus, if the query 112 is referring to a book, the userprobably wants to know the number of words or pages in the book. If, onthe other hand, the query 112 is referring to one of the movies, thefilm's running time is probably of interest. Or if “Harry Potter” refersto the fictional character, then length may refer to his height.

The method 100 can identify candidate topics 114 a-d as possibleinterpretations of the subject of the query 112. For instance, inresponse to receiving the Harry Potter query 112, candidate topics 114a-d include, among other topics, HARRY POTTER AND THE PHILOSOPHER'SSTONE (book) (114 a); HARRY POTTER AND THE PHILOSOPHER'S STONE (film)(114 b); HARRY POTTER AND THE DEATHLY HALLOWS (book) (114 c); and HARRYPOTTER AND THE DEATHLY HALLOWS, PART II (film) (114 d). Additionaltopics corresponding to the query 112 can also be identified.

The candidate topics 114 a-d are each identified from a plurality ofknown topics that can be stored in a knowledge base 222, such as the onedescribed below in reference to FIGS. 2 and 5. The knowledge base 222can contain information about a wide range of topics, attributesassociated with each topic, and relationships among the topics. In oneexample implementation of the knowledge base 222, the topics can berepresented by nodes in a graph of interconnected nodes. For example,one node can represent the book HARRY POTTER AND THE PHILOSOPHER'SSTONE, which was identified as candidate topic 114 a. The topic can havevarious attributes associated with the node. Some of the attributes maybe common to a type or category of the topic. For example, since thetopic relates to a book, the book's attributes can include genre, ISBNnumber, length, publication date, author, publisher, characters, andsales data. Others attributes can be unique to the particular topicregardless of its type. The knowledge base 222 includes facts that havebeen identified for some or all of the attributes for a topic. Forexample, it can include factual data that indicates HARRY POTTER AND THEPHILOSOPHER'S STONE is 309 pages long and was first published in 1997 byBloomsbury. On the other hand, topics relating to films may have adifferent set of attributes such as a running time, release date,actors, directors, budget, production studio, etc. Some of theattributes can form relationships among topics. For example, a “filmadaptation” attribute of the first HARRY POTTER book (candidate topic114 a) can connect the book topic 114 a to the corresponding film topic114 b. The knowledge base 222 can obtain information to build itsknowledge of topics, attributes, and relationships in any number ofways, such as crawling and scraping information from electronic resource224 s across one or more networks. In some implementations, informationcan also be provided to the knowledge base 222 manually by authorizedsystem operators or through a crowdsourcing operation.

The method 100 can determine possible interpretations of the query 112and identify candidate topics 114 from the knowledge base 222 thatcorrespond with these interpretations. In FIG. 1, for example, thecandidate topics 114 a-d are each determined to relate to the phrase“Harry Potter” from the query 112. The candidate topics 114 a-d can beidentified in one implementation by comparing a string or substring fromthe query 112 with strings or substrings of the topics and attributeinformation in the knowledge base 222. Using the words “Harry Potter”from the query 112, topics can be identified by searching the knowledgebase 222, or an index of the knowledge base 222, for the string “HarryPotter” or variant strings such as “harry potter” or “harry poter.”Using this technique, each of candidate topics 114 a-d can be identifiedfrom the knowledge base 222 since each of the topics 114 a-d include“Harry Potter” within strings associated with the topic including thebook and film names.

In some implementations, candidate topics 114 a-d can be identifiedusing one or more other techniques. In one implementation, a node in thedata graph that represents a topic can be identified as a candidatetopic 114 using, for example, string comparison, and further candidatetopics can be identified based on relationships/connections between thenodes. For example, the book HARRY POTTER AND THE DEATHLY HALLOWS isinitially identified as candidate topic 114 c based on the common phrase“Harry Potter” in the name of the book and the query 112. The film HARRYPOTTER AND THE DEATHLY HALLOWS, PART II can be identified as candidatetopic 114 d based on string comparison as well, or based on a connectionbetween topics 114 c and 114 d in the knowledge base 222 reflecting thatthe film is an adaptation of the book. Thus, if another example query112 asked, “Where was California's governor born?”, candidate topics 114could be identified by first matching the word “California” with aquestion topic that represents the state of California in the knowledgebase 222, and then identifying one or more intermediate topics that areconnected to the state of California from which an answer could beidentified. For instance, California may have a “Governor” attributethat links the State's topic with topics for each person who has beengovernor of the state. The candidate topics 114 can then be identifiedfrom the intermediate topics as people who were or a person who ispresently governor of California such as Jerry Brown, ArnoldSchwarzenegger, or Ronald Reagan.

Other implementations for identifying candidate topics 114 thatcorrespond to a subject of a query 112 are also contemplated. Forexample, if a string matching algorithm yields a large number ofcandidate topics 114 from the knowledge base 222, a most relevant subsetof the candidate topics 114 can be determined. In one implementation,the subset can correspond to topics that are associated with resource224 s such as web pages and other electronic documents or files that arereferenced by search results generated using the query 112. For example,a query that asked for “John Smith's height” might return dozens or morerelevant topics using string comparison algorithms with topics in theknowledge base 222. But by identifying only the John Smiths that arementioned in top-ranked search results, a most relevant subset can beselected as the candidate topics 114 that the user is most likelyreferring to in the query 112. In some implementations, the mostrelevant subset can be predicted and identified by a score associatedwith each topic in the knowledge base 222. The topic score can indicatea measure of the particular topic's prominence or popularity, forexample, based on its prevalence in web sites, recent news articles,and/or social media trends. The topic score can also be user-specific toreflect that some topics may be more or less relevant to particularindividual users or groups of users than to members of the generalpublic. For instance, a query 112 with the subject, “Washington,” mayyield many topics in the knowledge base 222 that have “Washington”included in a name or other relevant attribute for the topic. Based onthe prevalence of literature about George Washington in one or morecorpora of data such as the internet, the George Washington topic mayhave a higher topic score than a topic, for example, of WashingtonCounty, Minnesota. Therefore, George Washington can be identified as acandidate topic 114 before the Minnesota county. However, if the methoddetermines that the query 112 originated from a user who lives inWashington County, Minnesota, the topic score for the county may beadjusted for that user such that Washington County is more likely to beselected among the most relevant subset of candidate topics 114 for thatuser.

The method 100 can generate a candidate topic-answer pair 118 for eachcandidate topic 114. Each candidate-topic answer pair 118 includes ananswer 116. The answers 116 can be identified, for example, frominformation stored in the knowledge base 222. In some cases, the answers116 can be identified from an attribute associated with a respectivecandidate topic 114. For example, candidate topic 114 a is HARRY POTTERAND THE PHILOSOPHER'S STONE (book). One attribute of the book inknowledge base 222 is the book's page length, which is 309 pages. Thecorresponding answer 116 a to candidate topic 114 a is therefore 309pages. Together, candidate topic 114 a and answer 116 a form a candidatetopic-answer pair 118 a. Similarly, candidate topic 118 b—HARRY POTTERAND THE PHILOSOPHER'S STONE (film)—has a running time attribute of 152minutes that is identified as being the relevant answer 116 b to a query112 about the length of a film. Together, candidate topic 114 b andanswer 116 b form candidate topic-answer pair 118 b. The same patterncan apply in generating candidate topic-answer pairs 118 c and 118 d.

In addition to these facts, e.g., page length, running time, answers 116can also be actual topics in the knowledge base 222, which may beconnected to a candidate topic 114. For example, in a query 112 thatasked “Who wrote Harry Potter?”, HARRY POTTER AND THE PHILOSOPHER'SSTONE (book), is identified as a candidate topic 118 regarding thequestion topic or query subject. One attribute of the book is itsauthor, J. K. Rowling. However, unlike the answers 118 a-d depicted inFIG. 1, which are topics or nodes in the knowledge base 222 themselves,J. K. Rowling is a separate topic that can be represented by a node inthe knowledge base 222 and that is connected to the PHILOSOPHER'S STONE(book) topic.

Once an answer 116 has been determined for each candidate topic 114, andcorresponding candidate-topic answer pairs 118 generated, the method 100can submit the query 112 to a search engine 216, such as that describedbelow with reference to FIG. 2. The search engine 216 can obtain searchresults 120 in response to the query by searching, for example, one ormore corpora of data on one or more networks for resource 224 s that arerelevant to the query 112. The resource 224 s can include web pages, websites, blogs, PDFs, or any other electronic document, for example. Insome implementations, the search results 120 can be identified fromsearching content of the resource 224 s directly. Search results 120 canalso be obtained from an index 230 of resource 224 s that havepreviously been crawled and indexed. An index 230 can be useful to asearch engine 216 to enable more efficient searching of relevant datathat has been scraped and structured into a database prior to receivinga search query, particularly when large amounts of data, such as theinternet, provides the corpora of searchable resource 224 s.

Referring again to FIG. 1, the method 100 obtains multiple searchresults 120 a-c based on query 112 using, for example, the search engine216. The search results can each reference a resource 224 that has somerelevance to the query 112. For example, the top three search results120 a-c depicted in FIG. 1 are, respectively, J. K. Rowling's OfficialWebsite, a Harry Potter Fan Club Website, and a website for Harry Potterthe Ride. The resource 224 referenced by the top search result 120 a, J.K. Rowling's Official Website, includes content that notes J. K. Rowlingis the “Author of worldwide best-selling books including HARRY POTTERAND THE PHILOSOPHER'S STONE.” Based on an evaluation of content, one ormore annotations 122 can be determined that identify likely topicsassociated with a resource 224 120. The identified topics in theannotations 122 can be among the same topics stored and structured inthe knowledge base 222 from which the candidate topics 118 wereidentified. Therefore, one or more annotations 122 associated with thesearch results 120 can correspond to one or more of the candidate topics118 in that a common topic occurs in both the annotation 122 and acandidate topic 118.

For example, based on the occurrence of HARRY POTTER AND THEPHILOSOPHER'S STONE in the text of J. K. Rowling's Official website (120a), an annotation 122 a is created that associates the site 120 a withthe topic for the corresponding book in the knowledge base 222.Similarly, the second-ranked search result 120 b, which references theHarry Potter Fan Club website, contains text from a member's post thatasks, “Wasn't DEATHLY HALLOWS a scary movie?” From this content, the fanclub website is annotated (122 b) with the topic that indicates the filmHARRY POTTER AND THE DEATHLY HALLOWS, PART II. A resource 224 can havemultiple annotations 122 associated with it as well. For example, thereference to the movie in the member's post on the fan club website 120b could refer to either PART I or PART II of the DEATHLY HALLOWS films.Although not depicted, an annotation 122 could be associated with thesite for each film. A resource 224 might also explicitly relate to twoor more topics, and multiple annotations 122 can be associated with eachresource 224 to reflect the occurrence of multiple topics. For instance,if J. K. Rowling's website listed or discussed each of the books thatshe had authored, separate annotations 122 can be provided to associateher official website 120 a with a topic for each listed book.

In some implementations, evaluating resource 224 content and creatingannotations 122 can be automated, such as by a computer system thatcrawls resource 224 s in one or more corpora of data and across one ormore networks, similar to indexing by the back end 226 of the searchengine 216. In some implementations, evaluating resource 224 content andcreating annotations can also be performed manually. Evaluating contentof a resource 224 can include evaluating anything related to the pagesuch as text, images, advertising content, headings, the domain name,meta data, or more.

The method 100 can determine a score 124 a-d for each candidatetopic-answer pair 118 a-d. The score 124 can indicate a probablerelevance of each candidate-topic answer pair 118 to the query 112. Forexample, since the subject “Harry Potter” in query 112 is subject tomultiple interpretations, the score for each candidate topic-answer pair118 can quantify the likelihood that each candidate-topic answer pair118 would provide a correct interpretation of the query 112 that a userintended to find. In some implementations, the query 112 is notambiguous and only one candidate topic 118 is identified and onecandidate topic-answer pair 118 generated. The score 124 for the pair118 may then indicate how relevant a response from the Q&A method 100 isto the query 112.

The scores 124 can be based on one or more factors including anoccurrence of the candidate topic 114 in the annotations 122 of theresource 224 s referenced by one or more of the search results 120. Forexample, candidate topic 114 d, HARRY POTTER AND THE DEATHLY HALLOWS,PART II (film), occurs in annotations 122 b and 122 c for the resource224 s of two of the search results 120 b and 120 c. From this match, themethod 100 can infer that corresponding candidate topic-answer pair 118d likely has some relevance to the query 112, and therefore the pair's118 d score 124 d can be adjusted by an amount that reflects suchrelevance. For example, the scores 124 a-d can start with an initialscore of zero and can be increased when one or more factors indicatethat their respective candidate topic-answer pairs 118 a-d are relevantto the query 112. Thus, the two annotations 122 b and 122 c thatcorrespond to candidate-topic 118 d increase the score 124 d from 0 to50. Candidate topic 114 a, HARRY POTTER AND THE PHILOSOPHER'S STONE(book) occurs in annotation 122 a that is associated with the searchresult 120 a for J. K. Rowling's Official website. With only onecorresponding annotation 122, its score is raised to 20. However,candidate topics 114 b and 114 c do not occur in any of the annotations122 a-c, and their scores remain zero.

In some implementations, the scores 124 can be based on one or moreother or additional factors. For instance, the score 124 for a givencandidate topic-answer pair 118 can be based on an occurrence of theanswer 116 in annotations 122 of the resource 224 s referenced by theone or more search results 120. Thus, if the query 112 asked, “Who wroteHarry Potter and the Chamber of Secrets?”, candidate topics 114 mightinclude both the book and film for HARRY POTTER AND THE CHAMBER OFSECRETS. The corresponding answers 116 for the book and film topics areJ. K. Rowling (author) and Steve Kloves (screenplay writer),respectively. If most of the search results 120 generated in response tothe query 112 reference annotated resource 224 s that identified J. K.Rowling as a topic associated with the resource 224 s, whereasrelatively few identified Steve Kloves, then the score 124 for the bookcan be skewed higher than the score 124 for the film.

Scores can also be based on an occurrence of the candidate topic 114and/or the answer 116 being directly in the resource 224 s referenced bythe one or more search results 120 rather than or in addition toannotations 122 of the resource 224. For example, some resource 224 smay not have an annotation 122 associated with it that matches an answer116 in one of the candidate topic-answer pairs 118. But if it is knownthat the resource 224 references one of the candidate topics 114, andthe answer 116 that corresponds to the topic 114 is provided in contentassociated with the resource 224, then the resource 224 is more likelyto be relevant to the query 112 and the score 124 can be adjustedaccordingly to reflect the likely relevance. For example search result120 b has one annotation 122 b associated with it that identifies thetopic HARRY POTTER AND THE DEATHLY HALLOWS, PART II (film). There is noannotation 122 with the film's length that matches answer 116 d. Onereason can be that film running times are not represented as topics inknowledge base 222. If a fan club member posted text on the site saying,“At just 130 minutes, DEATHLY HALLOWS was too short for me!”, then themethod can use the occurrence of answer 116 d in the text or othercontent of the site to skew the film's score 124 b higher. Resource 224content can be evaluated for the occurrence of answers directly aftersearch results 120 are obtained, or identified from a search index ofpreviously crawled resource 224 s, for example.

In some implementations, scores 124 for candidate topic-answer pairs 118can be based in part on the relevance of the search results 120 to thequery 112. Search results 120 can be useful for comparing to candidatetopics 114 and answers 116 to disambiguate a query 112 and/or todetermine whether to respond to a query 112 with one or more candidatetopic-answer pairs 118. The method can leverage a search engine'salgorithms that are often sophisticated and finely-tuned to score orrank query interpretations for answering factual questions. Searchresults 120 are typically ranked according to relevance and presented indescending order of relevance. For example, the top results for a searchfor “Obama” relate to President Barack Obama, whereas lower-rankedresults relate to First Lady Michelle Obama, and their children Maliaand Sasha Obama. President Obama is predicted to be most relevant basedon, for example, the relatively large amount of resource 224 s, websites, news articles, social media content, and links about him.President Obama's selection can be further based, for example, onhistorical data that indicates most people who have searched for “Obama”tend to visit resource 224 s related to President Obama.

Based on the relevance of the search results 120, a query relevancescore 130 can be determined for each result 120 that indicates howrelevant each result is to the query 112. Because ranking can be basedon relevance, higher ranked search results 120 can have higher queryrelevance scores 130. The query relevance score 130 can be based onother factors as well, such as an absolute measure of relevance of aresult 120 to the query 112 (whereas ranking indicates relativerelevance). In some implementations, the scores 124 of candidatetopic-answer pairs 118 are based on query relevance scores 130 of thesearch results 120 that reference a resource 224 with an annotation 122in which the candidate topic 114 occurs. For example, search results 120a-c are depicted in descending order of relevance as determined bysearch engine 216. J. K. Rowling's Official Website (120 a) is thetop-ranked result in response to a query for “How long is Harry Potter.”The Harry Potter Fan Club Website (120 b) and Harry Potter the Ride site(120 c) are the second and third most relevant, respectively. The J. K.Rowling website has a query relevance score 130 a of “10 out of 10,” theFan Club Website has a query relevance score 130 b of “8 out of 10,” andthe Harry Potter the Ride website has a query relevance score 130 c of“7 out of 10.” The scores 124 a-c for the candidate topic-answer pairs118 a can be adjusted at least partly as a function of the queryrelevance scores 130 a-c. A highly relevant search result 120 (i.e.,high query relevance score 130) with an associated annotation 122 thatincludes one of the candidate topics 114 can indicate that thecorresponding candidate topic-answer pair 118 would be an appropriateresponse to the query 112. For instance, the score 124 a ofcandidate-topic answer pair 118 a can increase based on the top-rankedsearch result 120 a referencing J. K. Rowling's Official website, whichis annotated (122 a) with the candidate topic 114 a, HARRY POTTER ANDTHE PHILOSOPHER'S STONE (book).

The topic-answer pairs 118 can also be scored based on a confidencemeasure associated with the annotations 122 that can indicate howcertain or how likely it is that a resource 224 is related to anannotated topic 122. For example, the annotation 122 a for J. K.Rowling's Official Website 120 a includes the book topic HARRY POTTERAND THE PHILOSOPHER'S STONE. However, because the site as a whole isprimarily focused on J. K. Rowling rather than her individual books, alow confidence score 128 a is assigned to annotation 122 a of “3 out of10.” Although not depicted, the J. K. Rowling Website 120 a can haveadditional annotations 122 for different topics associated with thesite. An annotation 122 associated with “J. K. Rowling” in this instancewould have a higher confidence score 128 than the score 128 a for thebook. In another example, an annotation 122 c for the DEATHLY HALLOWS,PART II (film) is highly relevant to HARRY POTTER THE RIDE as describedin the website referenced by search result 120 c. For instance, the ridemay follow the storyline of the DEATHLY HALLOWS films. Accordingly, theannotation 122 c has a high confidence score of “8 out of 10.” Anotherannotation 122 that included the topic for the ride itself (notdepicted), might have an even higher confidence score 128, such as “10out of 10.” Sometimes, an annotated resource 224 may have only vaguereferences to topics included in its content. For example, a resource224 that mentions only “Harry Potter” without specifying a particularfilm, book, or other feature that is being discussed is vague. Anannotation 122 for such a resource 224 can be created that associates itwith a particular book, but the confidence score 128 for the annotation122 may be low due to uncertainty in determining the correct topic.

The method 100 can determine a score 124 for each of the candidatetopic-answer pairs 118 based on any combination of the factorsdescribed, as well as additional factors. For example, the pair 124 dassociated with the DEATHLY HALLOWS, PART II film is shown to have ascore 124 d of 50, which is highest among all of the candidatetopic-answer pairs 118 a-d, and therefore can be identified as thecandidate pair 118 that is most relevant to answering the query 112. Itshigh score 124 d can be attributed to the occurrence of the candidatetopic 114 d in two of the top three search results 120 b-c. Byimplication, a candidate pair 118 that has a candidate topic 114 thatoccurs in an annotation 122 for the top-ranked search result will notalways have the highest score 124. The candidates' scores 124 can bebased on multiple factors and combinations of factors.

The method 100 can use the candidate topic-answer scores 124 todetermine whether to respond to the query 112, and if so, whichcandidate topic-answer pair(s) 118 to respond with. The method onlyprovides a response 126 if one or more of the candidate topic-answerpairs 118 is determined to be sufficiently relevant to the query 112 towarrant a response. The method can determine whether to respond, forexample, by comparing the scores 124 of the candidate topic-answer pairs118 to a threshold score. Scores 124 that are below the threshold canindicate that the associated candidate topic-answer pair 118 is probablynot a good interpretation of the query 112. If the score 124 for eachcandidate topic-answer pair 118 is less than the threshold, then noresponse is provided from among the candidate-topic answer pairs 118. Ifthe score 124 of any candidate topic-answer pair 118 is equal to orexceeds the threshold, then a response is provided from among thecandidate topic-answer pairs 118 that satisfy the threshold. Forinstance, given a threshold score of 30, only candidate topic-answerpair 118 d has a score 124 d that exceeds the threshold, and it selectedto respond to the query 112 in response 126. The response 126 includesboth the topic 114 d and answer 116 d so that a consumer of the responsecan know the particular question that is being answered. Thus, theresponse can unambiguously state, “The movie HARRY POTTER AND THEDEATHLY HALLOWS, PART II is 130 minutes long.”

In some implementations, determining whether to respond can be donewithout comparing the scores 124 to a threshold. For example, the methodmay always respond with one or more of the candidate topic-answer pairs118 so long as a pair is identified. The system can also simply respondwith the top-scoring candidate pair 118 or several of the top-scoringcandidates 118 without regard to a threshold.

FIG. 2 depicts an example Q&A system 200 that can determine whether andhow to respond with an answer 116 to a query 112. In someimplementations, the system 200 can implement the example method 100depicted in FIG. 1. The system 200 includes a front-end module 210 thatcan coordinate data flow and control among the various system modules,receive a query 112, and determine how to respond to a query 112 withone or more answers 126. The system 200 also includes a mapping module212 for identifying candidate topics 114, an answer generator 214 fordetermining an answer 116 for each candidate topic 114, a knowledge base222, a search engine 216 that obtains search results 120 based on thequery 112, and a scoring module 220 that determines a score 124 for thecandidate topic-answer pairs 118. In some implementations, system 200can also include a back-end system 201 that can crawl, index, and/orannotate network resources 224. The back-end system 201 can include acrawling module 226, an annotator module 228, and an index 230.

In one example of the Q&A system 200, the front-end module 210 receivesa query 112 over network 208. The query 112 can be submitted by a user202 at a client computing device and transmitted over network 208. Thefront-end module 210 interfaces with the network 208 to receive thequery 112. The query 112 can be in the form of a string of charactersthat represent one or more words that can be a phrase, clause, sentence,or question, for example. The user 202 can submit the query in anapplication that is configured to interface with the Q&A system 200, orat a general search tool that can feed the query 202 to one or moredifferent systems such as the Q&A system 200, a local search engine onthe client computing device, and/or an internet search engine. Thesearch tool or another application at the client computing device or ata remote server can then determine which of the systems that receivedthe query will provide the most relevant results to the user 202 basedon various signals associated with the query 112. The user 202 cancommunicate with the Q&A system 200 over network 208, which can include,for example, the Internet, a local area network (LAN), a wide areanetwork (WAN), a virtual private network (VPN), and/or a wirelessnetwork such as WIFI, a cellular telephone network, or a 3G/4G datanetwork. In some implementations, a user 202 can submit a query 112 atthe Q&A system itself without transmitting the query 112 from a remoteclient computing device and through a network 208.

The front-end module 210 receives the query and can determine how toprocess the query 112. In some instances, the front-end 210 maypre-process the query 112, which may include removing whitespace,correcting spelling errors, converting between languages, transcribing avoice query to text, and/or structuring the query 112 into a particularformat. The front-end module 210 can then submit query 112, whether asreceived or after pre-processing, to the mapping module 212.

Mapping module 212 can identify one or more candidate topics 114 basedon the query 112. For example, for a query 112 that asked, “What is thepopulation of Peru?”, the mapping module 212 can identify one or morecandidate topics 114 as possible interpretations of “Peru,” the subjectof the query 112. The mapping module 212 may identify the country ofPeru, the city Peru, Nebr., and the city Peru, Ind. as candidate topics114 for the “Peru” query 112.

The mapping module 212 can map a query 112 to candidate topics 114 usingone or more algorithms. In some implementations, the mapping module 112can determine possible topics 114 using string-matching algorithms. Thecandidate topics 114 can be identified from among one or more entitiesin knowledge base 222. The knowledge base 222 is a repository of factsand information about various entities. Because the candidate topics 114can be selected from knowledge base 222 entities, the entities can alsobe referred to as topics or known topics. As depicted and described ingreater detail in FIG. 5, the knowledge base 222 can include a datagraph 500 of interconnected nodes 502 that each represent entities inthe data graph. The entities can each include one or more attributesthat define particular facts for that entity. Some attributes can formedges 504 in the data graph 500 that indicate relationships betweenmultiple entities. For example, the knowledge base 222 may have anentity for the country Peru. Population, for instance, can be anattribute associated with the country Peru, and its value may be29,399,817 based on data from 2011. Another attribute may indicate thecapital of Peru, and connect the entity for Peru to a separate entityfor the city of Lima as the capital of Peru. As the data graph 500 caninclude a large, increasing number of entities and associated attributesfor each entity, it can provide the knowledge base 222 with asubstantial repository of information that can form the library fromwhich information is drawn for identifying candidate topics 114 andanswers 116 in the Q&A system 200.

In some instances, the mapping module 212 can use additional and/orother techniques for identifying the candidate topics 114 based on thequery 112. For example, the mapping module 212 can use other informationthat is associated with the query 112 and/or user 202 to identifycandidate topics 114 that are initially determined to be irrelevant to aparticular user 202. For example, if the Q&A system 200 receivedinformation that indicated a current or default location of the user 202or the client computing device that submitted query 112, the mappingmodule 212 may exclude entities with a location that is greater than athreshold distance from the user 202. Other information that can be usedin identifying possible entities to include or exclude from thecandidate topics 114 may include information about the relevance orranking of search results in which one or more possible entities occur,information collected from other users about commonly searched entities,information about how helpful particular entities have been in answeringdifferent queries with similar subjects for the user 202 or other users,and information about the user 202 derived from past behavior or anonline profile of the user 202. The user 202 can choose to not shareinformation with the Q&A system 200 beyond the query 112 and the user202 may configure the Q&A system 200 to not store or use informationabout the user 202 to protect the user's 202 privacy.

The answer generator 214 determines an answer 116 for each candidatetopic 114. The answer generator 214 may receive information about thequery 112 and the candidate topics 114 that were identified by themapping module 212. The answer generator 214 can then use thisinformation as parameters in identifying an answer for each candidatetopic 114. For example, in the query 112 that asked for the populationof “Peru,” the mapping module 212 determined three candidate topics 114:Peru the country, the city of Peru, Ind., and the city of Peru, Nebr.The answer generator 214 determines that the populations of thesecandidate topics 114 are, respectively, 29.4 million, 11,417, and 865based on data in years 2010 and 2011. The answer generator 214 canidentify answers by accessing information from the knowledge base 222.For example, for each candidate topic 114, the answer generator canquery the knowledge base 222 for a particular attribute of the knowledgebase 222 entity that corresponds to the candidate topic 114. Theparticular attribute requested by answer generator 216 is based on thequery 112. Thus, when the query 112 relates to population, the answergenerator 216 can interpret the query 112 and retrieve an appropriateattribute for the candidate topic 114 from knowledge base 222. In theexample query 112, the answer generator 116 would request the attributecorresponding to population for each of the candidate topics 114. Insome implementations, the relevant attribute may differ among differenttypes of candidate topics 114. For instance, in a query 112 that asked,“How long is Harry Potter”, length is the relevant attribute, but itsmeaning can differ depending on the type of candidate topic 114. Forbooks, length may refer to the number of pages or words in the book. Butfor a film adaptation of the books, length may refer to the film'srunning time. The answer generator 214 can disambiguate the relevantattribute for each candidate topic 114 by, for example, referencing atable of synonyms that correlates attribute names in the knowledge base222 with other terms that can be used to indicate the attributes.

Using the answers 116 identified from knowledge base 222, the answergenerator 214 can also generate, for each candidate topic 114, acandidate topic-answer pair 118 that includes both the candidate topic114 and the answer 116 that has been identified for the topic 114. Thecandidate topic-answer pairs 118 can then be scored and one or more ofthe candidate topic-answer pairs 118 can be provided to the user 202based at least in part on the scores 124.

The search engine 216 can obtain search results 120 based on the query112 for the system 200 to use in determining a score for the candidatetopic-answer pairs 118. The search results 120 may reference variousresources 224 including electronic documents, PDFs, websites, images,audio files, and/or webpages. One or more of the referenced resources224 may be an annotated resource, which is a resource 224 that has anannotation 122 associated with the resource 224 that identifies one ormore likely topics associated with the resource 224. The annotations 122can include one or more topics from the knowledge base 222. For example,the official website for Peru, Ind. may have an annotation 122 thatindicates the page is related to Peru, Ind., which may be a topic orentity represented by a node 502 in the data graph 500 in knowledge base222. A resource 224 can also have multiple annotations 122 associatedwith the resource 224, for instance, when the resource 224 is likelyrelated to more than one topic.

The search engine 216 can have a back-end subsystem 201 that crawls,indexes, and/or annotates resources 224 across one or more systems ornetworks. The back-end subsystem 201 may include a crawling module 226that crawls the Internet, for example, and indexes resources 224encountered in the crawl. In some implementations, the crawling module226 accesses a first resource that links to one or more other resources.The crawling module 226 may load the first resource and store its textin index 230, which is a database that includes information about thecrawled resources 224 and their content, such as text, that isassociated with the resources 224. Once the crawling module 226 storesinformation about the first resource in index 230, it can then load theother resources 224 that are linked from the first resource and repeatthe indexing process. By repeatedly accessing and indexing resources 224linked from other resources 224, the crawling module 226 can capture andindex a substantial portion of the resources 224 that are accessible tothe crawling module 226. The crawling module 226 can crawl and index theresources 224 even before a query 112 is received by the Q&A system 200or before search engine 216 submits the query 112 to obtain searchresults 120. The search engine 216 can then obtain search results 120 bysearching index 230 rather than directly searching the resources 224.

The crawling module 226 can also interface with annotator 228. As thecrawling module 226 crawls resources 224, the annotator 228 can generateannotations 122 for one or more of the resources 224. In someimplementations, the annotator 228 can perform an automatic evaluationof a resource 224, including, for instance, content associated with theresource 224, and determine one or more likely topics from knowledgebase 222 that are likely associated with the resource 224. For example,if a given resource 224 was the official website for the New YorkYankees, the annotator 228 may identify three topics from knowledge base222 that are likely associated with the site: The New York Yankees MLBball club, baseball, and New York City. The annotations 122 can bestored in knowledge base 222 or in a separate annotations database thatreferences the knowledge base 222. Furthermore, the annotator 228 candetermine a confidence score 128 for each annotation 122 that indicatesthe likely strength of the association between the resource 224 and theannotation 122. In the Yankees example, the annotation 122 for the NewYork Yankees MLB ball club may have a high confidence score 128 becausethe website is the official site for the Yankees and is primarilyrelated to the Yankees franchise. The annotations 122 that identifybaseball and New York City as topics may each have lower confidencescores 128 because the Yankees site is less directly related to thesetopics, even though they are relevant to the site to some degree. Insome situations, the annotator 228 may not be able to discern any topicthat a particular resource 224 is about, and therefore the annotator 228may not generate any annotation 122 for that resource, or it maydetermine one or more annotations 122 that have low confidence scores128 because of the uncertainty.

The search engine 216 can also include a results scorer 218, which is amodule that may determine and assign a query relevance score 130 tosearch results 120 that the search engine 218 obtains based on the query112. The query relevance score 130 can indicate how relevant each result120 is to the query 112. Because ranking can be based on relevance,higher-ranked search results 120 can have higher query relevance scores130. The query relevance score 130 can be based on other factors aswell, such as an absolute measure of relevance of a result 120 to thequery 112, whereas ranking indicates relative relevance. For example,based on the query 112 that asked, “What is the population of Peru?”,the top search result 120 may reference the official website for thegovernment of the South American country of Peru. Because the searchengine 116 determined that the government website was the most relevantresource 224 based on the query 112, the results scoring module 218 mayassign a high query relevance score 130 to the search result 120. If thetop fifteen search results 120 each referenced websites related to thecountry of Peru, and only lower-ranked search results 120 referencedresources 224 pertaining to the cities of Peru in either Indiana orNebraska, then results scorer 218 may assign lower query relevancescores 130 to the search results 120 related to these American cities.

System 200 also includes a scoring module 220 that determines a score124 for each candidate topic-answer pair 118. The score 124 for eachcandidate topic-answer pair 124 can be based on one or more factorsincluding an occurrence of the candidate topic 114 in the annotations122 of the resources 224 referenced by one or more of the search results120, an occurrence of the answer 116 in annotations 122 of the resources224 referenced by one or more of the search results 120, and/or anoccurrence of the candidate topic 114 or answer 116 in the resources 224referenced by one or more of the search results 120. The candidate pairscores 124 can indicate how likely it is that each candidatetopic-answer pair 118 will be responsive to the query 112. The system200 can use the scores 124 to determine whether to respond to a query112 and in selecting one or more pairs 118 to respond with. Thus, insome instances when the query 112 is vague or ambiguous, the score canbe used to disambiguate a query 112 by indicating which topic-answerpairs 118 that the user 202 is most likely interested in.

In some implementations, higher candidate pair scores 124 can indicatemore likely relevant candidate topic-answer pairs 118, and the lowercandidate pair scores 124 can indicate less likely relevant candidatetopic-answer pairs 118. The candidate pair scores 124 may each startwith an initial score 124 of zero, and as various factors indicate thata given candidate topic-answer pair 118 is more likely to be relevant tothe query 112, the scoring module 220 can increase the candidate pairscore 124 by some amount.

The scoring module 220 can increase a candidate pair score 124 when thecandidate topic 114 in a respective candidate topic-answer pair 118occurs in the annotations 122 of the resources 224 reference by one ormore of the search results 120. For instance, in the example query 112for the population of Peru, if the top two search results 120 obtainedby search engine 216 for the query 112 were the country of Peru'sofficial website and an online encyclopedia page about the country ofPeru, the candidate pair score 124 for the pair 118 that includes thecandidate topic 114 for the country of Peru can be increased based oneach website having an associated annotation 122 that identified thecountry of Peru. In some implementations, the scoring module 220 canincrease the candidate pair score 124 based on each annotated resource224 referenced by search results 120 that is associated with anannotation 122 in which the candidate topic 114 occurs. Thus, thescoring module 220 may increase the score 124 for the candidatetopic-answer pair 118 that relates to the country of Peru by a firstamount based on the country of Peru's official website having anannotation 122 for the country of Peru, and also increased by anadditional amount based on the encyclopedia page annotation 122 for thecountry of Peru.

The scoring module 220 can also score the candidate topic-answer pairs118 based on occurrences of the answers 116 in the annotations 122 ofthe resources 224 referenced by one or more of the search results 120.For example, if the query 112 asked about the leader of Peru rather thanpopulation, the top search result 120 generated by search engine 216 inresponse to query 112 may again be the official government website forthe country of Peru. The website may have a first annotation 122 thatidentifies a topic of the website as the country of Peru. However,annotator 228 may have also determined that the website is related, to alesser degree, to Ollanta Humala, the President of Peru, and generated asecond annotation 122 that identifies Ollanta Humala as a topic of thesite. Answer generator 214 may have also identified Ollanta Humala asthe answer 116 to the query 112 for the candidate topic 114 of thecountry of Peru. The occurrence of the answer 116 for a candidatetopic-answer pair 118 in an annotation 122 of a resource 224 referencedby one or more search results 120 can cause the scoring module 220 toincrease the score 124 for a candidate topic-answer pair. Thus, scoringmodule 220 may increase the country of Peru's candidate pair score 124to reflect the fact that Ollanta Humala occurs in both answer 116 forthe pair 118 and in the annotation 122 for the official governmentwebsite.

The scoring module 220 can also generate candidate pair scores 124 basedon an occurrence of the candidate topic 114 and/or answer 116 in theresources 224 that one or more of the search results 120 reference. Evenwhen a resource 224 has not been annotated with a candidate topic 114 oranswer 116, the fact that it was referenced by search engine 216 insearch results 120 can indicate that it still may be relevant to thescore 124 for a candidate topic-answer pair 118. For instance, if thecontent of the resource 224 references the candidate topic 114 or answer116, the scoring module 220 can increase the candidate pair score 124for a corresponding pair 118 based on such content. Sometimes, answers116 determined by the answer generator 214 may not correspond to topicsin knowledge base 222 that annotator 228 can generate an annotation 122for. For example, the answer 116 for the population of the country ofPeru is approximately 30.1 million people. While population is anattribute for the entity Peru in knowledge base 222, the populationitself may not be an entity in knowledge base 222 and therefore may notbe subject to annotation. However, if the top two search results 120reference pages that include text about the population of the country ofPeru, the scoring module 220 can use that text to infer that thecandidate topic-answer pair 118 for the country of Peru is likely to berelevant to query 112 and increase the candidate pair score 124accordingly.

The scoring module 220 can determine candidate pair scores 124 based onany combination of the factors described, as well as additional factors.In some implementations, the scoring module 220 can also determinecandidate pair scores 124 based on the query relevance scores 130 of theone or more search results 120. For example, in response to the query112, “What is the population of Peru?”, search engine 216 may obtainsearch results 120 that include a top-ranked search result 120 thatreferences a first website for the country of Peru, and anineteenth-ranked search result 120 that references a second website forthe city of Peru, Nebr. The search result 120 for the first website mayhave a high query relevance score 130 and the second website may have arelatively lower query relevance score 130. Using these query relevancescores 130, the scoring module 220 can skew the candidate pair score 124for the country of Peru higher than the candidate topic-answer pair 118for Peru, Nebr. because the site about the country was more relevant tothe query 112 in the search results 120 than the site about theNebraskan city. The query relevance scores 130 thus provide a signal toscoring module 220 that certain search results 120 are likely to be morerelevant to the query 112 than others and these differences can beaccounted for by varying the impact that differently ranked searchresults 120 have on the candidate pair scores 124.

In some implementations, the scoring module 220 can also base thecandidate pair scores 124 on the confidence scores 128 associated withannotations 122. The confidence scores 128 indicate an estimatedstrength of the correlation between an annotation 122 and the resource224 that it annotates. An annotated resource 224 that is certainly andprimarily related to the topic identified in an associated annotation122 may have a higher annotation confidence score 128, whereas anannotated resource 224 that less clearly relates to the topic identifiedin an annotation 122 may have a lower confidence score 128. The scoringmodule 220 can use the occurrence of candidate topics 114 or answers 116in annotations 122 of resources 224 referenced by one or more searchresults 120 to determine candidate pair scores 124. The higher that theconfidence scores 128 are for the annotations 122 that the scoringmodule 220 uses to adjust the candidate pair scores 124, the scoringmodule 220 can increase the candidate pair scores 124 to a greaterdegree than if the confidence scores 124 were lower.

The front-end module 210 can determine whether to respond to the query112 with one or more of the candidate topic-answer pairs 118 based onthe candidate pair scores 124. The front-end only provides a response126 if the system 200 determines that one or more of the candidatetopic-answer pairs 118 is sufficiently relevant to the query 112 towarrant a response. The front-end 210 can determine whether to respond,for example, by comparing the scores 124 of the candidate topic-answerpairs 118 to a threshold score. Scores 124 that are below the thresholdcan indicate that its associated candidate topic-answer pair 118 isprobably not a good interpretation of the query 112. If the score 124for each candidate topic-answer pair 118 is less than the threshold,then no response is provided from among the candidate topic-answer pairs118. If the score 124 of any candidate topic-answer pair 118 is equal toor exceeds the threshold, then the front-end 210 can respond from amongthe candidate topic-answer pairs 118 that satisfy the threshold. In someimplementations, the front-end 210 can determine whether to respond toquery 112 without comparing the scores 124 to a threshold. For example,the front-end 210 can be configured to always respond with one or moreof the candidate topic-answer pairs 118 so long as any pair at all isidentified by the mapping module 212. The system 200 can also respondwith just the top-scoring candidate pair 118 or several of thetop-scoring candidates 118 without regard to a threshold.

FIG. 3 depicts an example method 300 for determining whether and how torespond to a query 112 with one or more topic-answer pairs 118.

The method 300 can start by annotating resources 224, as shown inoperation 304. The resources 224 can be electronic documents such asPDFs, webpages, text documents, or multimedia files, for example, andcan be located in one or more corpora of data in a private network oracross one or more public networks such as the internet Annotation canbe performed by the annotator 228 as shown in FIG. 2 as part of aback-end subsystem 201 for a search engine 216 or Q&A system 200.Annotating resources 224 can include an automatic or manual evaluationof content associated with the resources 224 to determine one or moretopics that one or more of the resources likely pertains to. Theannotated topics may correspond to topics stored in a factual repositorysuch as knowledge base 222, and which can be represented by nodes 502 ina data graph 500 of interconnected nodes 502 that each represent a topicin the knowledge base 222.

At operation 306, the method 300 includes receiving query 112. The query112 can be submitted by a user 202 at a client computing device andtransmitted over a network 208. The method 300 can also receive queries112 at the Q&A system 200 rather than from a remote client device. Thequery 112 can be of any form such as a natural language query and thequery 112 may comprise one or more words that may be a phrase, clause,question, or sentence. The query 112 may include a request for an answer116 to a question in the query 112. In some implementations, the method300 may perform one or more pre-processing operations to convert thequery 112 to a suitable form for processing in other portions of themethod 300. For example, spelling or grammatical corrections can bemade, a query subject identified or isolated, and/or the query 112 maybe converted to a different language. The operations at stage 306 can beperformed, for example, by the front-end module 210 in Q&A system 200.

At operation 308, the method 300 identifies candidate topics 114 basedon the query 112. The candidate topics 114 can be identified from topicsin the knowledge base 222. Method 300 can identify the candidate topics114 using one or more techniques. In some implementations, the method300 determines one or more words from the query 112 that may be thesubject or object of the query 112, for example. The method 300 cancompare strings or substrings of the one or more words from query 112with strings or substrings of topic names from knowledge base 222 toidentify corresponding topics in the knowledge base 222 as candidatetopics 114. The method 300 may also limit the number of candidate topics114 that it identifies and it may aim to identify only those topics thatare most likely to be relevant to query 112. A subset of the mostrelevant candidate topics 114 can be determined by comparing topics thathave been identified using string-matching algorithms to search results120 based on the query 112 and/or based on information in knowledge base222 or a search index 230 that indicates which topics user 202 or otherusers have found most helpful based on past queries 112. The mappingmodule 212 can in some implementations perform the operations in stage308 of method 300.

At 310, the method 300 generates candidate topic-answer pairs 118 foreach candidate topic 114 identified by the method at 308. Answergenerator 214 can be used in some implementations to perform theoperations at 310. For each candidate topic 114, the method 300identifies an answer 116 that the method 300 determines may beresponsive to query 112. The method 300 may identify the answers 116from knowledge base 222. The answers 116 can correspond to an attributevalue of the candidate topics 114 in knowledge base 222. Some answers116 can occur as a topic that is represented by a node in the data graph500 in knowledge base 222.

Once the method 300 has identified answers 116 for each candidate topic114, the method 300 can generate candidate topic-answer pairs 118 thateach includes a candidate topic 114 and an answer 116.

At stage 312, the method 300 can obtain search results 120 based on thereceived query 112. Search results 120 can be obtained, for example, bya search engine 216 such as that depicted in FIG. 2. The method 300 canobtain search results 120 by searching one or more corpora of data onone or more networks for resources 224 that are relevant to the query112. In some implementations, the method 300 can search an index 201that includes data from resources 224 that have previously been crawledby an automated bot such as crawling module 226. The index 201 can beconfigured to provide the search engine 216 with results much quickerthan searching the content of resources 224 directly, particularly inlarge networks like the internet in which there may be billions ortrillions of indexable resources 224.

The search results 120 obtained by the method 300 can each reference aresource 224 that has some relevance to the query 112. One or more ofthe referenced resources 224 may be annotated with one or more topicsthat are likely associated with the resource 224 as described in stage304 of the method 300.

At stage 314, the method 300 can determine a candidate pair score 124for each of the candidate topic-answer pairs 118. The scores 124 canindicate to the method 300 which of the candidate topic-answer pairs 118are most likely relevant or responsive to the received query 112. Scores124 can be based one or more factors or combinations of factors. Forexample, the method 300 may increase a score 124 for a candidatetopic-answer pair 118 in which the candidate topic 114 occurs in anannotation 122 of a resource 224 referenced by one or more searchresults 120. If a candidate topic 114 occurs in annotations 122 formultiple resources 224 in multiple search results 120, the score 124 maybe increased a greater amount than if the topic 114 occurred in just oneof the resources 224 for a single search result 120. The scores 124 canalso be based on an occurrence of the answer 116 for a candidatetopic-answer pair 118 in an annotation 122 for a resource 224 referencedby the search results 120. An annotation 122 of the answer 116 canindicate that the resource 224 associated with the annotation 122 issomehow related to the answer 116. Thus, the presence of a search result120 referencing such resource 224 indicates that the answer 116 isrelevant to the query 112, and the method 300 can increase thecandidate-pair score 124 accordingly. In some implementations, themethod 300 can base the score 124 for one or more of the candidatetopic-answer pairs 118 on an occurrence of the answer 116 in theresources 224 referenced by the search results 120 rather than, or inaddition to, an occurrence of the answer 116 in an annotation 122. Thismay be useful, for example, when a resource 224 includes the answer 116in content of the resource 224, but the answer 116 may not correspond toa topic in knowledge base 222, or the method 300 may have determined notto annotate the resource 224 with the answer 116 because the resource isprimarily about other topics. Even when the answer 116 is not in anannotation 122, its presence in a resource 224 referenced by the searchresults 120 indicate that the resource 224 may still be relevant to thequery 112.

In some implementations, the method 300 can also base scores 124 for thecandidate topic-answer pairs 118 on other factors. In one example, themethod 300 can determine a query relevance score 130 for one or more ofthe search results 120 that indicate how relevant each search result 120is to the query 112. The query relevance score 130 for each searchresult 120 may have a relative component and/or an absolute component.The relative component of the query relevance score 130 can indicatewhere each search result 120 ranked among the obtained search results120. For instance, the top-ranked search result 120 is usuallydetermined by a search engine 216 that implements the method 300 to bethe most relevant search result 120 to the query 112, and would have acorrespondingly higher query relevance score 130 than lower-rankedsearch results 120. However, if none of the search results 120 areparticularly relevant to the query 112, then an absolute component ofthe query relevance score 130 can cause the score 124 to be lower. Themethod 300 can then make the candidate pair scores 124 at leastpartially a function of the query relevance scores 130. For example, ifthe method 300 determines to increase the score 124 for a candidate pair118 because the candidate topic 114 occurs in an annotation 122 of aresource 224 reference by a search result 120, the amount that the pairscore 124 is increased can depend on the query relevance score 130 ofthe search result 120. Thus, if the candidate topic 114 occurs in anannotation 122 associated with a top-ranked search result, then thecandidate topic 114 is likely to be very relevant to the query 112 andthe candidate pair score 124 can be increased more than if theannotation 122 in which the candidate topic 114 occurred was associatedwith a resource 224 referenced by a lower-ranked search result 120.

The method 300 can also base the score 124 for the candidatetopic-answer pairs 118 on a confidence score 128 associated with theannotations 122. Confidence scores 128 indicate the likelihood that anannotated resource 224 is actually related to the topic identified in anannotation 122 associated with the resource 224. If the content of theresource 224 is primarily related to the topic occurring in anassociated annotation 122, then the method 300 may assign a highconfidence score 128 to the annotation 122. However, if at stage 304 themethod 300 was unable to discern whether the annotated topic is really afocus of the page, or if the method 300 determines that the topic isrelevant but not a strong focus of the page, then the method 300 mayassign the annotation 122 for the resource 224 a low confidence score128. The method 300 can then factor the confidence scores 128 into itsdetermination of the candidate pair scores 124. When the method 300bases the candidate pair scores 124 on annotations 122 that have higherconfidence scores 128, the method 300 can increase the candidate pairscores 124 a higher amount than if the candidate pair scores 124 arebased on annotations 122 that have lower confidence scores 128.

At stage 316, the method 300 can then determine whether to show ananswer 116 from one or more of the candidate topic-answer pairs 118. Tomake this determination, the method 300 may compare each of the scores124 for the candidate topic-answer pairs 118 to a threshold score. Ifany of the candidate pair scores 124 satisfy the threshold, then themethod can determine to respond to the query 112 with an answer 116 andproceed to stage 318. However, if the none of the candidate topic-answerpairs 118 have a score 124 that satisfies the threshold score, then themethod 300 may choose not to show an answer 116 in response to the query112 because the scores 124 indicate that none of the candidatetopic-answer pairs 118 are sufficiently relevant to the query 112. Insome implementations, the threshold score can be adjusted by a user orthe method 300 may determine an appropriate threshold based onhistorical indications of how responsive past answers 116 have been topast queries 112. The method 300 can also be configured to always showat least one answer 116, regardless of whether any of the candidate pairscores 124 satisfy a threshold score. If the method 300 determines thatit will not respond to the query 112 with an answer 116, then the method300 may end at stage 322.

If the method 300 determines that it will respond to the query 112 withan answer 116, then the method 300 proceeds to stage 318 in which themethod 300 can select one or more answers 116 to show. The method 300can choose which of the answers 116 to respond with based on the scores124 for the candidate topic-answer pairs 118. For example, the method300 may respond with only the answer 116 in the candidate topic-answerpair 118 that has the highest candidate pair score 124. It can alsorespond with any of the answers 116 that have an associated candidatetopic-answer pair 118 with a score 124 that satisfies the thresholdscore. In some implementations, the method 300 may receive user inputthat directs how the method 300 should select which answers 116 torespond with, such as whether to respond with one answer 116 or multipleanswers 116.

After the method 300 selects one or more answers 116 to respond to thequery 112 with, the method 300 shows the selected answers 116 at stage320. The method 300 can present the selected answers 116 to a user at aclient computing device, or can transmit the answers 116 over a network208 for a client computing device to process in any way, including, forexample, presenting the answers 116 at an interface for a Q&A system200. In some implementations, the method 300 may show both the selectedanswers 116 and their corresponding candidate topics 114 so that theuser can know precisely what question is being answered in response tothe query 112.

FIG. 4 is an example search result page 400 that shows a response 126and search results 420 according to one implementation of the Q&Asystems and methods that this document describes. A user 202 is shown tohave submitted a query 412 that asks “how tall is Obama.” The query 412is ambiguous because “Obama” may refer to any person named Obama,including anyone in the First Family. Thus, the mapping module 212 inthe Q&A system 200 or stage 308 in the method 300 may identify PresidentBarack Obama, First Lady Michelle Obama, and First Children Sasha Obamaand Malia Obama as candidate topics 114 for the query 412. The knowledgebase 222 can provide an answer 116 for each candidate topic 114, thusforming candidate topic-answer pairs 118.

A search engine 216 obtains search results 420 for the query 412. Thesearch results 420 each reference a resource 224, one or more of whichmay be annotated. The search results 420 are shown in descending orderof relevance to the query 112. Thus, the top-ranked search result 420 ais about Malia Obama's height. The second-ranked search result 420 b isan online encyclopedia webpage about the heights of U.S. presidents, andthree of the following four search results 420 c-f link to pages aboutthe First Lady or First Children rather than President Obama.

The search results page 400 also includes a factual response 426 thatanswers the query 412. The response 426 indicates that the topic 114selected by the Q&A system 200 or methods 100 or 300 is President BarackObama and that his height is 6 feet, 1 inch. This example shows thateven though the top search result 420 a, and several of the top fewsearch results 420 a-f that the search engine 216 generated in responseto the query 412 are about members of the President's family rather thanthe President himself, the system 200 or methods 100 or 300 determinedthat President Obama's height was the most relevant response 426 to thequery 412. This can happen because the systems and methods describedherein may select a candidate topic-answer pair 118 based on a number offactors relating to the search results 420, annotations 122 of resources224 referenced by the search results 420, and the content of theannotated resources 224, for example. More search results 420 related toBarack Obama and his height than top-ranked Malia Obama, for example,and therefore the Q&A system 200 or methods 100 or 300 selected answer116 relating to Barack Obama to respond to the query 412.

FIG. 5 is a data graph 500 in accordance with an example implementationof the techniques described herein. The data graph 500 may represent thestorage and structure of information in the knowledge base 222. Such adata graph 500 stores nodes 502 and edges 504, from which a graph, suchas the graph illustrated in FIG. 5 can be created. The nodes 502 may bereferred to as topics or entities, and the edges 504 may be referred toas attributes, which form connections between two topics. In someimplementations, candidate topics 114, answers 116, and annotations 122can correspond to one or more of the topics represented by nodes 502 inthe data graph 500.

Topics and attributes in the data graph 500 may be stored in knowledgebase 222 in a number of ways. In one example, the knowledge base 222stores triples, also referred to as tuples, that represent the topicsand attributes or connections. A triple may include a <subject;predicate; object> format, with the subject representing a startingtopic, the predicate representing an outward edge from the subject, andthe object representing the topic pointed to by the outward edge. Forexample, in FIG. 5, one example of a triple is the entity Tom Hanks asthe subject, the relationship acted in as the predicate, and the entityLarry Crowne as the object. Of course, a data graph 500 with a largenumber of topics and even a limited number of relationships may havebillions of triples.

FIG. 6 is a block diagram of computing devices 600, 650 that may be usedto implement the systems and methods described in this document, aseither a client or as a server or plurality of servers. Computing device600 is intended to represent various forms of digital computers, such aslaptops, desktops, workstations, personal digital assistants, servers,blade servers, mainframes, and other appropriate computers. Computingdevice 650 is intended to represent various forms of mobile devices,such as personal digital assistants, cellular telephones, smartphones,and other similar computing devices. Additionally computing device 600or 650 can include Universal Serial Bus (USB) flash drives. The USBflash drives may store operating systems and other applications. The USBflash drives can include input/output components, such as a wirelesstransmitter or USB connector that may be inserted into a USB port ofanother computing device. The components shown here, their connectionsand relationships, and their functions, are meant to be exemplary only,and are not meant to limit implementations described and/or claimed inthis document.

Computing device 600 includes a processor 602, memory 604, a storagedevice 606, a high-speed interface 608 connecting to memory 604 andhigh-speed expansion ports 610, and a low speed interface 612 connectingto low speed bus 614 and storage device 606. Each of the components 602,604, 606, 608, 610, and 612, are interconnected using various busses,and may be mounted on a common motherboard or in other manners asappropriate. The processor 602 can process instructions for executionwithin the computing device 600, including instructions stored in thememory 604 or on the storage device 606 to display graphical informationfor a GUI on an external input/output device, such as display 616coupled to high speed interface 608. In other implementations, multipleprocessors and/or multiple buses may be used, as appropriate, along withmultiple memories and types of memory. Also, multiple computing devices600 may be connected, with each device providing portions of theoperations (e.g., as a server bank, a group of blade servers, or amulti-processor system).

The memory 604 stores information within the computing device 600. Inone implementation, the memory 604 is a volatile memory unit or units.In another implementation, the memory 604 is a non-volatile memory unitor units. The memory 604 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 606 is capable of providing mass storage for thecomputing device 600. In one implementation, the storage device 606 maybe or contain a computer-readable medium, such as a floppy disk device,a hard disk device, an optical disk device, or a tape device, a flashmemory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied inan information carrier. The computer program product may also containinstructions that, when executed, perform one or more methods, such asthose described above. The information carrier is a computer- ormachine-readable medium, such as the memory 604, the storage device 606,or memory on processor 602.

The high speed controller 608 manages bandwidth-intensive operations forthe computing device 600, while the low speed controller 612 manageslower bandwidth-intensive operations. Such allocation of functions isexemplary only. In one implementation, the high-speed controller 608 iscoupled to memory 604, display 616 (e.g., through a graphics processoror accelerator), and to high-speed expansion ports 610, which may acceptvarious expansion cards (not shown). In the implementation, low-speedcontroller 612 is coupled to storage device 606 and low-speed expansionport 614. The low-speed expansion port, which may include variouscommunication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet)may be coupled to one or more input/output devices, such as a keyboard,a pointing device, a scanner, or a networking device such as a switch orrouter, e.g., through a network adapter.

The computing device 600 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 620, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system 624. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 622. Alternatively, components from computing device 600 may becombined with other components in a mobile device (not shown), such asdevice 650. Each of such devices may contain one or more of computingdevice 600, 650, and an entire system may be made up of multiplecomputing devices 600, 650 communicating with each other.

Computing device 650 includes a processor 652, memory 664, aninput/output device such as a display 654, a communication interface666, and a transceiver 668, among other components. The device 650 mayalso be provided with a storage device, such as a microdrive or otherdevice, to provide additional storage. Each of the components 650, 652,664, 654, 666, and 668, are interconnected using various buses, andseveral of the components may be mounted on a common motherboard or inother manners as appropriate.

The processor 652 can execute instructions within the computing device650, including instructions stored in the memory 664. The processor maybe implemented as a chipset of chips that include separate and multipleanalog and digital processors. Additionally, the processor may beimplemented using any of a number of architectures. For example, theprocessor 610 may be a CISC (Complex Instruction Set Computers)processor, a RISC (Reduced Instruction Set Computer) processor, or aMISC (Minimal Instruction Set Computer) processor. The processor mayprovide, for example, for coordination of the other components of thedevice 650, such as control of user interfaces, applications run bydevice 650, and wireless communication by device 650.

Processor 652 may communicate with a user through control interface 658and display interface 656 coupled to a display 654. The display 654 maybe, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display)display or an OLED (Organic Light Emitting Diode) display, or otherappropriate display technology. The display interface 656 may compriseappropriate circuitry for driving the display 654 to present graphicaland other information to a user. The control interface 658 may receivecommands from a user and convert them for submission to the processor652. In addition, an external interface 662 may be provide incommunication with processor 652, so as to enable near areacommunication of device 650 with other devices. External interface 662may provide, for example, for wired communication in someimplementations, or for wireless communication in other implementations,and multiple interfaces may also be used.

The memory 664 stores information within the computing device 650. Thememory 664 can be implemented as one or more of a computer-readablemedium or media, a volatile memory unit or units, or a non-volatilememory unit or units. Expansion memory 674 may also be provided andconnected to device 650 through expansion interface 672, which mayinclude, for example, a SIMM (Single In Line Memory Module) cardinterface. Such expansion memory 674 may provide extra storage space fordevice 650, or may also store applications or other information fordevice 650. Specifically, expansion memory 674 may include instructionsto carry out or supplement the processes described above, and mayinclude secure information also. Thus, for example, expansion memory 674may be provide as a security module for device 650, and may beprogrammed with instructions that permit secure use of device 650. Inaddition, secure applications may be provided via the SIMM cards, alongwith additional information, such as placing identifying information onthe SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory,as discussed below. In one implementation, a computer program product istangibly embodied in an information carrier. The computer programproduct contains instructions that, when executed, perform one or moremethods, such as those described above. The information carrier is acomputer- or machine-readable medium, such as the memory 664, expansionmemory 674, or memory on processor 652 that may be received, forexample, over transceiver 668 or external interface 662.

Device 650 may communicate wirelessly through communication interface666, which may include digital signal processing circuitry wherenecessary. Communication interface 666 may provide for communicationsunder various modes or protocols, such as GSM voice calls, SMS, EMS, orMMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others.Such communication may occur, for example, through radio-frequencytransceiver 668. In addition, short-range communication may occur, suchas using a Bluetooth, WiFi, or other such transceiver (not shown). Inaddition, GPS (Global Positioning System) receiver module 670 mayprovide additional navigation- and location-related wireless data todevice 650, which may be used as appropriate by applications running ondevice 650.

Device 650 may also communicate audibly using audio codec 660, which mayreceive spoken information from a user and convert it to usable digitalinformation. Audio codec 660 may likewise generate audible sound for auser, such as through a speaker, e.g., in a handset of device 650. Suchsound may include sound from voice telephone calls, may include recordedsound (e.g., voice messages, music files, etc.) and may also includesound generated by applications operating on device 650.

The computing device 650 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as acellular telephone 680. It may also be implemented as part of asmartphone 682, personal digital assistant, or other similar mobiledevice.

Various implementations can be realized in digital electronic circuitry,integrated circuitry, specially designed ASICs (application specificintegrated circuits), computer hardware, firmware, software, and/orcombinations thereof. These various implementations can includeimplementation in one or more computer programs that are executableand/or interpretable on a programmable system including at least oneprogrammable processor, which may be special or general purpose, coupledto receive data and instructions from, and to transmit data andinstructions to, a storage system, at least one input device, and atleast one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems, methods, apparatus, and techniques described here can beimplemented in a computing system that includes a back end component(e.g., as a data server), or that includes a middleware component (e.g.,an application server), or that includes a front end component (e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the systemsand techniques described here), or any combination of such back end,middleware, or front end components. The components of the system can beinterconnected by any form or medium of digital data communication(e.g., a communication network). Examples of communication networksinclude a local area network (“LAN”), a wide area network (“WAN”),peer-to-peer networks (having ad-hoc or static members), grid computinginfrastructures, and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular implementations of particularinventions. Certain features that are described in this specification inthe context of separate implementations can also be implemented incombination in a single implementation. Conversely, various featuresthat are described in the context of a single implementation can also beimplemented in multiple implementations separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular implementations of the subject matter have beendescribed. Other implementations are within the scope of the followingclaims. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results. In certain implementations, multitasking andparallel processing may be advantageous.

What is claimed is:
 1. A computer-implemented method comprising:identifying one or more candidate topics from a query; generating, foreach candidate topic, a candidate topic-answer pair that includes (i)the candidate topic, and (ii) an answer to the query for the candidatetopic; obtaining search results based on the query, wherein one or moreof the search results references an annotated resource, wherein anannotated resource is a resource that, based on an automated evaluationof the content of the resource, is associated with an annotation thatidentifies one or more likely topics associated with the resource; foreach candidate topic-answer pair, determining a score for the candidatetopic-answer pair based on (i) an occurrence of the candidate topic inthe annotations of the resources referenced by one or more of the searchresults, and (ii) an occurrence of the answer in annotations of theresources referenced by the one or more search results, or in theresources referenced by the one or more search results; and determiningwhether to respond to the query with one or more answers from thecandidate topic-answer pairs, based on the scores.
 2. Thecomputer-implemented method of claim 1, wherein determining whether torespond to the query comprises comparing one or more of the scores to apredetermined threshold; and the method further comprises selecting,based on the comparison of the one or more scores to the predeterminedthreshold, one or more answers to respond to the query from among thecandidate topic-answer pairs.
 3. The computer-implemented method ofclaim 1, wherein determining whether to respond to the query comprisescomparing one or more of the scores to a predetermined threshold; andthe method further comprises determining, based on the comparison of theone or more scores to the predetermined threshold, to not respond to thequery with an answer from among the candidate topic-answer pairs.
 4. Thecomputer-implemented method of claim 1, further comprising selecting,based on the scores, one or more answers to respond to the query fromamong the candidate topic-answer pairs.
 5. The computer-implementedmethod of claim 4, wherein the one or more selected answers includes ananswer from the candidate topic-answer pair that has a highest scoreamong the scores of each of the candidate topic-answer pairs.
 6. Thecomputer-implemented method of claim 4, further comprising providing theselected one or more answers to a user at a client computing device. 7.The computer-implemented method of claim 1, wherein each candidate topicis represented by a node in a graph of interconnected nodes that eachrepresents a known topic.
 8. The computer-implemented method of claim 7,wherein generating, for each candidate topic, a candidate topic-answerpair that includes (i) the candidate topic, and (ii) an answer to thequery for the candidate topic, comprises identifying an attribute valuefrom the node that represents each candidate topic, respectively.
 9. Thecomputer-implemented method of claim 7, wherein the answer in one ormore of the candidate topic-answer pairs is represented by a node in thegraph of interconnected nodes.
 10. The computer-implemented method ofclaim 1, wherein, for one or more of the candidate topic-answer pairs,the score is further based on a respective query relevance score of thesearch results that include annotations in which the candidate topicoccurs.
 11. The computer-implemented method of claim 1, wherein, for oneor more of the candidate topic-answer pairs, the score is further basedon a confidence measure associated with (i) each of one or moreannotations in which the candidate topic in a respective candidatetopic-answer pair occurs, or (ii) each of one or more annotations inwhich the answer in a respective candidate topic-answer pair occurs. 12.The computer-implemented method of claim 1, wherein: the obtained searchresults are ranked according to a determined relevance of each of thesearch results to the query; and for each candidate topic-answer pair,the score for the candidate topic-answer pair is determined furtherbased on the determined relevance to the query of at least one searchresult from the obtained search results that references an annotatedresource for which the candidate topic or the answer from the candidatetopic-answer pair occurs in the annotation for the annotated resource.13. The computer-implemented method of claim 1, wherein, for eachcandidate topic-answer pair, the score for the candidate topic-answerpair is determined further based on a number of the search results thatreference an annotated resource for which the candidate topic or theanswer from the candidate topic-answer pair occurs in the annotation forthe annotated resource.
 14. The computer-implemented method of claim 1,wherein the search results are identified separately from the candidatetopics and the answers in the candidate topic-answer pairs.
 15. Acomputer system comprising: one or more computing devices; an interfaceat the one or more computing devices that is programmed to receive aquery; a knowledge repository that is accessible to the one or morecomputing devices and that includes a plurality of topics, each topicincluding one or more attributes and associated values for theattributes; a mapping module that is installed on the one or morecomputing devices and that identifies one or more candidate topics fromthe topics in the knowledge repository, wherein the identified candidatetopics are determined to relate to a possible subject of the query; ananswer generator that is installed on the one or more computing devicesand that generates, for each candidate topic, a candidate topic-answerpair that includes (i) the candidate topic, and (ii) an answer to thequery for the candidate topic, wherein the answer for each candidatetopic is identified from information in the knowledge repository; asearch engine that is installed on the one or more computing devices andthat returns search results based on the query, wherein one or more ofthe search results references an annotated resource, wherein anannotated resource is a resource that, based on an automated evaluationof the content of the resource, is associated with an annotation thatidentifies one or more likely topics associated with the resource; ascoring module that is installed on the one or more computing devicesand that determines a score for each candidate topic-answer pair basedon (i) an occurrence of the candidate topic in the annotations of theresources referenced by one or more of the search results, and (ii) anoccurrence of the answer in annotations of the resources referenced bythe one or more search results, or in the resources referenced by theone or more search results; and a front end system at the one or morecomputing devices that determines whether to respond to the query withone or more answers from the candidate topic-answer pairs, based on thescores.
 16. The computer system of claim 15, wherein the front endsystem determines whether to respond to the query based on a comparisonof one or more of the scores to a predetermined threshold.
 17. Thecomputer system of claim 15, wherein each of the plurality of topicsthat is included in the knowledge repository is represented by a node ina graph of interconnected nodes.
 18. The computer system of claim 15,wherein one or more of the returned search results is associated with arespective query relevance score and the score determined by the scoringmodule for each candidate topic-answer pair is further based on thequery relevance scores of one or more of the search results thatreference an annotated resource in which the candidate topic occurs. 19.The computer system of claim 15, wherein, for one or more of thecandidate topic-answer pairs, the score is further based on a confidencemeasure associated with (i) each of one or more annotations in which thecandidate topic in a respective candidate topic-answer pair occurs, or(ii) each of one or more annotations in which the answer in a respectivecandidate topic-answer pair occurs.
 20. A tangible computer-readablestorage device having instructions stored thereon that, when executed byone or more computer processors, cause the processors to performoperations comprising: identifying one or more candidate topics from aquery; generating, for each candidate topic, a candidate topic-answerpair that includes (i) the candidate topic, and (ii) an answer to thequery for the candidate topic; obtaining search results based on thequery, wherein one or more of the search results references an annotatedresource, wherein an annotated resource is a resource that, based on anautomated evaluation of the content of the resource, is associated withan annotation that identifies one or more likely topics associated withthe resource; for each candidate topic-answer pair, determining a scorefor the candidate topic-answer pair based on (i) an occurrence of thecandidate topic in the annotations of the resources referenced by one ormore of the search results, and (ii) an occurrence of the answer inannotations of the resources referenced by the one or more searchresults, or in the resources referenced by the one or more searchresults; and determining whether to respond to the query with one ormore answers from the candidate topic-answer pairs, based on the scores.21. The tangible computer-readable storage device of claim 20, whereindetermining whether to respond to the query includes comparing one ormore of the scores to a predetermined threshold.
 22. The tangiblecomputer-readable storage device of claim 20, wherein the operationsfurther comprise determining a query relevance score for one or more ofthe returned search results, and wherein the score for one or more ofthe candidate topic-answer pairs is further based on the query relevancescores of one or more of the search results that reference an annotatedresource in which the candidate topic occurs.
 23. The tangiblecomputer-readable storage device of claim 20, wherein for one or more ofthe candidate topic-answer pairs, the score is further based on aconfidence measure associated with (i) each of one or more annotationsin which the candidate topic in a respective candidate topic-answer pairoccurs, or (ii) each of one or more annotations in which the answer in arespective candidate topic-answer pair occurs.